Fair value estimates for illiquid cryptocurrency

Guangyue Zhang, Alexander Sannella, Gerard Brennan and Muhammad Talha Afzal

International Journal of Accounting Information Systems, 2024, vol. 54, issue C

Abstract: To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.

Keywords: Cryptocurrency; Fair value measurement; Machine learning prediction; Cryptocurrency classification; Investors’ attention and sentiment analysis; Exogenous variables (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:54:y:2024:i:c:s1467089524000332

DOI: 10.1016/j.accinf.2024.100700

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